Stephen Yu PRO

Most "AI humanizers" are tuned against a detector. This one never saw one.

humanizer-gemma-4-e4b rewrites an AI draft so it reads like a person wrote it — while keeping every fact, number, name and date. The reward during RL was an LLM judge scoring fidelity against an atomic fact list of the draft, plus a penalty for reusing the draft's phrasing and syntax. No detector anywhere in the loop, by design: optimise against a classifier and you learn that classifier, not writing.

On a 39-case everyday-writing set (62 English samples, two independent judges, an error counts only if both report it):

- 0 / 62 critical fidelity errors (v1, SFT+DPO: 3 / 62)
- 0 samples copying > 35 % of the draft's 5-grams (baseline 4B rewriter: 33 / 93)
- reuse median 0.29 — the model rewrites, it does not shuffle
- Originality.ai rates 85 % of outputs "human" — measured once, after the fact, never optimised

Two things worth knowing before you try it: it is a base-model completion, not a chat model (the exact instruction wrapper ships in prompt_format.json — reproduce it byte for byte, and turn off Ollama/LM Studio's chat template), and it drops a qualifier in roughly 1 in 3 outputs ("an estimated 4.2 %" becomes "4.2 %"). Proofread numbers and the direction of every claim.

bf16 + GGUF (Q8_0 / Q6_K / bf16) in one repo. Q5 and below are withheld — fidelity collapses.

Try it: jialinyyzz/humanizer
Model: https://huggingface.co/jialinyyzz/humanizer-gemma-4-e4b
How it was trained: https://huggingface.co/blog/jialinyyzz/we-built-an-ai-humanizer-and-never-let-it-see-a-de
Code and eval set: https://github.com/sgaofen/humanizer

upvoted an article 16 days ago

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We built an AI humanizer and never let it see a detector

jialinyyzz

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16 days ago

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posted an update 16 days ago

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Most "AI humanizers" are tuned against a detector. This one never saw one.

humanizer-gemma-4-e4b rewrites an AI draft so it reads like a person wrote it — while keeping every fact, number, name and date. The reward during RL was an LLM judge scoring fidelity against an atomic fact list of the draft, plus a penalty for reusing the draft's phrasing and syntax. No detector anywhere in the loop, by design: optimise against a classifier and you learn that classifier, not writing.

On a 39-case everyday-writing set (62 English samples, two independent judges, an error counts only if both report it):

- 0 / 62 critical fidelity errors (v1, SFT+DPO: 3 / 62)
- 0 samples copying > 35 % of the draft's 5-grams (baseline 4B rewriter: 33 / 93)
- reuse median 0.29 — the model rewrites, it does not shuffle
- Originality.ai rates 85 % of outputs "human" — measured once, after the fact, never optimised

Two things worth knowing before you try it: it is a base-model completion, not a chat model (the exact instruction wrapper ships in prompt_format.json — reproduce it byte for byte, and turn off Ollama/LM Studio's chat template), and it drops a qualifier in roughly 1 in 3 outputs ("an estimated 4.2 %" becomes "4.2 %"). Proofread numbers and the direction of every claim.

bf16 + GGUF (Q8_0 / Q6_K / bf16) in one repo. Q5 and below are withheld — fidelity collapses.

Try it: jialinyyzz/humanizer
Model: https://huggingface.co/jialinyyzz/humanizer-gemma-4-e4b
How it was trained: https://huggingface.co/blog/jialinyyzz/we-built-an-ai-humanizer-and-never-let-it-see-a-de
Code and eval set: https://github.com/sgaofen/humanizer